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How to calculate gradient magnitude from row in numpy?

I am using the following code to calculate the gradient magnitude of a window which takes the gradient of each row.
g = array([f(x), f(x+1), f(x+2)])
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
fg = np.sum(g)
print(fg)
array([ 9, 24, 45, 64, 81, 98, 115, 132, 159, 174])

I am using this based on an answer from here.
numpy.abs(fg.reshape(-1).T)
array([9, 18, 27, 36, 45, 54, 63, 72, 81, 90])

This works fine, the issue is though that this requires an input of tens of thousands of elements, when I can get away with only a few hundreds.
To my understanding to get a magnitude of gradient, I would need to multiply each of these rows by -1 and then square. However I am unsure how to create this multiplication and square function in numpy. I know this is far from a numpy expert so any help would be most appreciated. Thanks

A:

In : x
Out: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Out: array([ 9., 24., 45., 64., 81., 98., 115., 132., 159., 174.])
Out: array([ 9., 81., 288., 648., 990., 1332., 1584., 2088., 2562.])

And the function:
c6a93da74d